Full Stack Developer (Expert) 1508

Our client in Pretoria is recruiting for a Full Stack Developer (Expert) to join their team.

• Design, build, test, deploy and operate secure, scalable and maintainable DVF software, data products and platform capabilities.
• Own technical delivery from discovery and design through implementation, release, monitoring, support and continuous improvement.
• Develop high-quality Python and Java components for data processing, APIs, microservices, automation, integration and platform services.
• Engineer reliable batch and event-driven data pipelines with clear contracts, validation, observability, lineage and recoverability.
• Build AI-enabled solutions and reusable engineering accelerators, including agents, RAG patterns, tools, prompts, context packages, evaluations and guardrails.
• Apply specification-driven and AI-assisted development practices, using human approval gates for
• architecture, security, quality and production changes.
• Take a systems view of DVF, identifying cross-subsystem impacts, dependencies, bottlenecks, failure modes and opportunities to simplify the landscape.
• Challenge conventional approaches, rapidly prototype options and turn validated ideas into governed, reusable patterns and production solutions.
• Produce and maintain architecture views, technical designs, interface contracts, decision records, runbooks and implementation documentation.
• Implement automated unit, integration, contract, component, performance and regression tests as appropriate.
• Build and maintain CI/CD pipelines, Infrastructure as Code, security scanning, dependency management and quality gates.
• Ensure solutions meet requirements for security, privacy, data governance, information protection, auditability, resilience and operational support.
• Use telemetry and operational feedback to improve reliability, performance, cost efficiency and developer experience.
• Lead code reviews and technical design reviews, mentor engineers and promote consistent engineering standards and reusable solutions.
• Collaborate with Product Owners, Systems Analysts, Architects, Data Engineers, Test Engineers, Operations and Security to resolve ambiguity and deliver measurable outcomes.
• Support PI planning and sprint execution by refining technical work, identifying dependencies and risks, and providing realistic implementation options.
• Communicate technical decisions, progress, risks and trade-offs clearly to technical and non-technical
• stakeholders.
• Contribute to the evolution of DVF into an AI-native, federated data-product organisation with shared platform, quality and AI enablement capabilities.

• Qualifications/Experience:
• Relevant degree or diploma in Computer Science, Software Engineering, Information Technology, Data Engineering or a related field.
• Typically seven or more years of relevant software engineering experience, including responsibility for complex production systems.
• Demonstrable expert-level delivery using Python and/or Java, with strong data and integration engineering experience.
• Practical evidence of AI-assisted or AI-native engineering, including evaluation, governance and accountable human review.
• AWS, Java, Python, data engineering, architecture, DevOps, Kubernetes, Terraform, Agile or AI-related certifications are advantageous.
• Essential Skills Requirements:
• Expert software engineering capability with a strong record of designing, building, testing, deploying and operating production-grade enterprise solutions.
• Advanced Python and Java development, including clean code, object-oriented and functional design, concurrency, performance optimisation, packaging and dependency management.
• Strong data engineering capability across batch and event-driven ingestion, ETL/ELT, data transformation, data modelling, data quality, metadata, lineage and large-scale processing.
• Hands-on experience with APIs, microservices and integration patterns, including REST, GraphQL, messaging, Kafka and contract-first development.
• Cloud-native engineering experience, preferably AWS, including serverless, containers, storage, databases, queues, identity, networking and observability.
• AI engineering capability covering LLM-based applications, agents, retrieval-augmented generation, prompt and context engineering, tool integration, evaluation, guardrails and human approval gates.
• Ability to use AI-assisted development responsibly across specification, coding, review, testing, documentation and operations while retaining accountability for correctness and security.
• Strong systems thinking: understand end-to-end behaviour, dependencies, feedback loops, failure modes, trade-offs and the wider organisational impact of technical decisions.
• Out-of-the-box problem solving with the ability to simplify complex problems, challenge assumptions, prototype alternatives and convert successful approaches into reusable patterns.
• Engineering excellence across automated testing, secure coding, code review, CI/CD, Infrastructure as Code, version control and production-readiness practices.
• Ability to translate business and system requirements into maintainable architecture, technical designs, implementation plans and measurable engineering outcomes.
• Clear communication and technical leadership across multidisciplinary, distributed and multicultural teams.
• Adaptability to perform additional tasks under the Agile Working Model (AWM) Charter.
• Advantageous Skills Requirements:
• Experience in automotive, mobility, manufacturing or a large multinational enterprise environment.
• Knowledge of enterprise data products, federated data platforms, Digital Twin concepts, semantic models, knowledge graphs and ontologies.
• Experience with Apache Spark, Kafka, SQL and NoSQL technologies, and modern lakehouse or warehouse patterns.
• Exposure to TypeScript/JavaScript and modern frontend frameworks such as Angular or React.
• Experience with Docker, Kubernetes, Terraform, GitHub Enterprise and GitHub Actions.
• Knowledge of observability, SRE, incident management, resilience engineering and service management practices.
• Understanding of privacy, information protection, data governance, retention, cross-border data handling and responsible AI controls.
• Ability to mentor engineers, lead technical design reviews and improve engineering standards across teams.
• High ownership, curiosity, integrity, resilience and a continuous-learning mindset.
• Willingness and ability to travel internationally when required.